Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks

24 May 2024

Papers citing "Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks"

7 / 7 papers shown

Title
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning Nilo Schwencke Cyril Furtlehner 66 1 0 14 Dec 2024
PETScML: Second-order solvers for training regression problems in Scientific Machine Learning Stefano Zampini Umberto Zerbinati George Turkyyiah David E. Keyes 40 4 0 18 Mar 2024
DOF: Accelerating High-order Differential Operators with Forward Propagation Ruichen Li Chuwei Wang Haotian Ye Di He Liwei Wang AI4CE 24 2 0 15 Feb 2024
Preconditioning for Physics-Informed Neural Networks Songming Liu Chang Su J. Yao Zhongkai Hao Hang Su Youjia Wu Jun Zhu AI4CE PINN 38 5 0 01 Feb 2024
ISAAC Newton: Input-based Approximate Curvature for Newton's Method Felix Petersen Tobias Sutter Christian Borgelt Dongsung Huh Hilde Kuehne Yuekai Sun Oliver Deussen ODL 31 5 0 01 May 2023
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 40 23 0 28 Jan 2022
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 222 0 26 Apr 2021